Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw con
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Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw con
library(HiCDCPlus)
outdir <- tempdir()
# Generate genomic features (GC, mappability, effective length)
construct_features(
output_path = paste0(outdir, "/hg19_50kb_GATC"),
gen = "Hsapiens",
gen_ver = "hg19",
sig = "GATC",
bin_type = "Bins-uniform",
binsize = 50000,
chrs = "chr21"
)
# Initialize gi_list from the bintolen file
gi_list <- generate_bintolen_gi_list(
bintolen_path = paste0(outdir, "/hg19_50kb_GATC_bintolen.txt.gz")
)
# Expand 1D features to 2D for modeling
gi_list <- expand_1D_features(gi_list)
Input: Genome version, restriction enzyme pattern, and bin size; Output: A gi_list object containing 2D genomic features ready for interaction calling.
HiCDCPlus or HiCDCPlus_parallel.hicdcdiff.construct_features.gi_list_topdom..hic files using extract_hic_eigenvectors.MACS2 or epigraHMM.DESeq2 or edgeR directly..hic, .matrix, or .allValidPairs formats.HiCDCPlus.hicdc2hic (e.g., 'pvalue', 'qvalue', 'normcounts', 'zvalue', 'raw').hicdcdiff.construct_features before initializing the gi_list to ensure GC and length biases are modeled.expand_1D_features prior to running the negative binomial regression.HiCDCPlus_parallel for efficient genome-wide interaction calling across multiple chromosomes.hic2icenorm_gi_list before running gi_list_topdom.HiCDCPlus; always call expand_1D_features first.hicdcdiff with biological replicates to ensure robust statistical power.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
日本語の概要は準備中です。原文の説明を表示しています。
This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
日本語の概要は準備中です。原文の説明を表示しています。
apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
日本語の概要は準備中です。原文の説明を表示しています。